Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.3 MiB
Average record size in memory552.4 B

Variable types

Text1
Numeric11
Categorical7

Alerts

blood_pressure is highly overall correlated with bp_hr_interaction and 1 other fieldsHigh correlation
bp_glucose_ratio is highly overall correlated with glucose_levelHigh correlation
bp_hr_interaction is highly overall correlated with blood_pressure and 3 other fieldsHigh correlation
cholesterol_level is highly overall correlated with pca2High correlation
duration_per_hr is highly overall correlated with pca1 and 1 other fieldsHigh correlation
glucose_level is highly overall correlated with bp_glucose_ratioHigh correlation
heart_rate is highly overall correlated with bp_hr_interactionHigh correlation
pca1 is highly overall correlated with blood_pressure and 2 other fieldsHigh correlation
pca2 is highly overall correlated with bp_hr_interaction and 1 other fieldsHigh correlation
symptom_duration is highly overall correlated with duration_per_hrHigh correlation
patient_id has unique values Unique
pca1 has unique values Unique
pca2 has unique values Unique
age has 108 (1.1%) zeros Zeros

Reproduction

Analysis started2025-04-14 16:52:38.488989
Analysis finished2025-04-14 16:52:48.044133
Duration9.56 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

patient_id
Text

Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size634.9 KiB
2025-04-14T22:22:48.337502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters80000
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st rowPAT00000
2nd rowPAT00001
3rd rowPAT00002
4th rowPAT00003
5th rowPAT00004
ValueCountFrequency (%)
pat00000 1
 
< 0.1%
pat00008 1
 
< 0.1%
pat00017 1
 
< 0.1%
pat00002 1
 
< 0.1%
pat00003 1
 
< 0.1%
pat00004 1
 
< 0.1%
pat00005 1
 
< 0.1%
pat00006 1
 
< 0.1%
pat00007 1
 
< 0.1%
pat00009 1
 
< 0.1%
Other values (9990) 9990
99.9%
2025-04-14T22:22:48.751081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 14000
17.5%
P 10000
12.5%
A 10000
12.5%
T 10000
12.5%
6 4000
 
5.0%
7 4000
 
5.0%
3 4000
 
5.0%
4 4000
 
5.0%
5 4000
 
5.0%
8 4000
 
5.0%
Other values (3) 12000
15.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 80000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 14000
17.5%
P 10000
12.5%
A 10000
12.5%
T 10000
12.5%
6 4000
 
5.0%
7 4000
 
5.0%
3 4000
 
5.0%
4 4000
 
5.0%
5 4000
 
5.0%
8 4000
 
5.0%
Other values (3) 12000
15.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 80000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 14000
17.5%
P 10000
12.5%
A 10000
12.5%
T 10000
12.5%
6 4000
 
5.0%
7 4000
 
5.0%
3 4000
 
5.0%
4 4000
 
5.0%
5 4000
 
5.0%
8 4000
 
5.0%
Other values (3) 12000
15.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 80000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 14000
17.5%
P 10000
12.5%
A 10000
12.5%
T 10000
12.5%
6 4000
 
5.0%
7 4000
 
5.0%
3 4000
 
5.0%
4 4000
 
5.0%
5 4000
 
5.0%
8 4000
 
5.0%
Other values (3) 12000
15.0%

age
Real number (ℝ)

Zeros 

Distinct90
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.4528
Minimum0
Maximum89
Zeros108
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:22:48.857793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q122
median44
Q367
95-th percentile85
Maximum89
Range89
Interquartile range (IQR)45

Descriptive statistics

Standard deviation25.9518
Coefficient of variation (CV)0.58380574
Kurtosis-1.2028159
Mean44.4528
Median Absolute Deviation (MAD)22.5
Skewness0.014441554
Sum444528
Variance673.49592
MonotonicityNot monotonic
2025-04-14T22:22:48.947183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 139
 
1.4%
61 129
 
1.3%
57 129
 
1.3%
12 128
 
1.3%
24 128
 
1.3%
86 128
 
1.3%
53 126
 
1.3%
25 126
 
1.3%
81 125
 
1.2%
20 125
 
1.2%
Other values (80) 8717
87.2%
ValueCountFrequency (%)
0 108
1.1%
1 117
1.2%
2 107
1.1%
3 108
1.1%
4 95
0.9%
5 106
1.1%
6 88
0.9%
7 113
1.1%
8 111
1.1%
9 113
1.1%
ValueCountFrequency (%)
89 124
1.2%
88 101
1.0%
87 111
1.1%
86 128
1.3%
85 118
1.2%
84 104
1.0%
83 111
1.1%
82 94
0.9%
81 125
1.2%
80 104
1.0%

gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size605.7 KiB
Female
4982 
Male
4825 
Other
 
193

Length

Max length6
Median length5
Mean length5.0157
Min length4

Characters and Unicode

Total characters50157
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female 4982
49.8%
Male 4825
48.2%
Other 193
 
1.9%

Length

2025-04-14T22:22:49.037303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:22:49.144231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
female 4982
49.8%
male 4825
48.2%
other 193
 
1.9%

Most occurring characters

ValueCountFrequency (%)
e 14982
29.9%
a 9807
19.6%
l 9807
19.6%
F 4982
 
9.9%
m 4982
 
9.9%
M 4825
 
9.6%
O 193
 
0.4%
t 193
 
0.4%
h 193
 
0.4%
r 193
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50157
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 14982
29.9%
a 9807
19.6%
l 9807
19.6%
F 4982
 
9.9%
m 4982
 
9.9%
M 4825
 
9.6%
O 193
 
0.4%
t 193
 
0.4%
h 193
 
0.4%
r 193
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50157
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 14982
29.9%
a 9807
19.6%
l 9807
19.6%
F 4982
 
9.9%
m 4982
 
9.9%
M 4825
 
9.6%
O 193
 
0.4%
t 193
 
0.4%
h 193
 
0.4%
r 193
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50157
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 14982
29.9%
a 9807
19.6%
l 9807
19.6%
F 4982
 
9.9%
m 4982
 
9.9%
M 4825
 
9.6%
O 193
 
0.4%
t 193
 
0.4%
h 193
 
0.4%
r 193
 
0.4%

blood_pressure
Real number (ℝ)

High correlation 

Distinct817
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.13804
Minimum62.2
Maximum187.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:22:49.340793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum62.2
5-th percentile95.7
Q1109.8
median120.2
Q3130.5
95-th percentile145.1
Maximum187.2
Range125
Interquartile range (IQR)20.7

Descriptive statistics

Standard deviation15.091376
Coefficient of variation (CV)0.12561697
Kurtosis-0.0085879674
Mean120.13804
Median Absolute Deviation (MAD)10.3
Skewness-0.011185183
Sum1201380.4
Variance227.74964
MonotonicityNot monotonic
2025-04-14T22:22:49.438175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120.3 39
 
0.4%
122.1 37
 
0.4%
123.8 36
 
0.4%
114.7 36
 
0.4%
118.8 35
 
0.4%
122.2 35
 
0.4%
117.8 35
 
0.4%
120.6 34
 
0.3%
115.1 34
 
0.3%
121.7 34
 
0.3%
Other values (807) 9645
96.5%
ValueCountFrequency (%)
62.2 1
< 0.1%
65.2 1
< 0.1%
65.5 1
< 0.1%
70 2
< 0.1%
70.1 1
< 0.1%
70.4 1
< 0.1%
70.6 1
< 0.1%
71.2 1
< 0.1%
71.8 1
< 0.1%
72 1
< 0.1%
ValueCountFrequency (%)
187.2 1
< 0.1%
179.1 1
< 0.1%
174.2 1
< 0.1%
174 1
< 0.1%
169.3 1
< 0.1%
167.1 2
< 0.1%
166.5 1
< 0.1%
166.1 1
< 0.1%
165 2
< 0.1%
164.7 1
< 0.1%

heart_rate
Real number (ℝ)

High correlation 

Distinct573
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.92768
Minimum30.3
Maximum112.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:22:49.530067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum30.3
5-th percentile58.795
Q168.1
median75
Q381.7
95-th percentile91.4
Maximum112.3
Range82
Interquartile range (IQR)13.6

Descriptive statistics

Standard deviation9.9705304
Coefficient of variation (CV)0.13306872
Kurtosis0.010943293
Mean74.92768
Median Absolute Deviation (MAD)6.8
Skewness0.012427005
Sum749276.8
Variance99.411477
MonotonicityNot monotonic
2025-04-14T22:22:49.617203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69.7 54
 
0.5%
76.8 52
 
0.5%
76.5 52
 
0.5%
75.9 50
 
0.5%
73.7 50
 
0.5%
68.9 49
 
0.5%
71.4 49
 
0.5%
71.9 48
 
0.5%
75.2 48
 
0.5%
74.4 48
 
0.5%
Other values (563) 9500
95.0%
ValueCountFrequency (%)
30.3 1
< 0.1%
39.7 1
< 0.1%
40 1
< 0.1%
40.5 1
< 0.1%
40.7 1
< 0.1%
41.3 1
< 0.1%
41.6 1
< 0.1%
43 1
< 0.1%
43.4 1
< 0.1%
44.1 1
< 0.1%
ValueCountFrequency (%)
112.3 1
< 0.1%
111.9 1
< 0.1%
111 1
< 0.1%
108.5 2
< 0.1%
107.6 1
< 0.1%
107.2 1
< 0.1%
106.6 2
< 0.1%
106.3 1
< 0.1%
105.4 1
< 0.1%
105.1 2
< 0.1%

glucose_level
Real number (ℝ)

High correlation 

Distinct1278
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.97652
Minimum9.2
Maximum188.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:22:49.700385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum9.2
5-th percentile58.895
Q182.775
median100
Q3117
95-th percentile140.9
Maximum188.4
Range179.2
Interquartile range (IQR)34.225

Descriptive statistics

Standard deviation25.04059
Coefficient of variation (CV)0.25046471
Kurtosis-0.069181733
Mean99.97652
Median Absolute Deviation (MAD)17.1
Skewness-0.015863818
Sum999765.2
Variance627.03115
MonotonicityNot monotonic
2025-04-14T22:22:49.790204image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96.7 27
 
0.3%
99.5 26
 
0.3%
106 25
 
0.2%
97.5 25
 
0.2%
93.4 24
 
0.2%
103.1 24
 
0.2%
109.6 23
 
0.2%
93.3 23
 
0.2%
103.4 23
 
0.2%
112.7 23
 
0.2%
Other values (1268) 9757
97.6%
ValueCountFrequency (%)
9.2 1
< 0.1%
13.8 1
< 0.1%
14.1 1
< 0.1%
14.3 1
< 0.1%
15.2 1
< 0.1%
18.7 1
< 0.1%
19.5 1
< 0.1%
19.7 1
< 0.1%
20 1
< 0.1%
20.5 1
< 0.1%
ValueCountFrequency (%)
188.4 1
< 0.1%
183.4 1
< 0.1%
182.4 1
< 0.1%
182 1
< 0.1%
179.8 1
< 0.1%
178.5 1
< 0.1%
178.3 1
< 0.1%
177.6 1
< 0.1%
175.5 1
< 0.1%
175.2 1
< 0.1%

cholesterol_level
Real number (ℝ)

High correlation 

Distinct1865
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180.04955
Minimum8.2
Maximum329.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:22:49.882606image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum8.2
5-th percentile113.8
Q1153.4
median180.3
Q3206.8
95-th percentile245.6
Maximum329.8
Range321.6
Interquartile range (IQR)53.4

Descriptive statistics

Standard deviation39.839699
Coefficient of variation (CV)0.22127075
Kurtosis-0.003167008
Mean180.04955
Median Absolute Deviation (MAD)26.7
Skewness-0.048719807
Sum1800495.5
Variance1587.2016
MonotonicityNot monotonic
2025-04-14T22:22:49.975397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
158.8 21
 
0.2%
201.8 18
 
0.2%
152.3 18
 
0.2%
191.7 18
 
0.2%
187 18
 
0.2%
185.1 17
 
0.2%
190.4 16
 
0.2%
198.7 16
 
0.2%
164.3 16
 
0.2%
178.1 16
 
0.2%
Other values (1855) 9826
98.3%
ValueCountFrequency (%)
8.2 1
< 0.1%
43.1 1
< 0.1%
47.3 1
< 0.1%
48.5 1
< 0.1%
49.4 1
< 0.1%
50.1 1
< 0.1%
50.5 1
< 0.1%
51 1
< 0.1%
52 1
< 0.1%
52.6 1
< 0.1%
ValueCountFrequency (%)
329.8 1
< 0.1%
316.1 1
< 0.1%
315.2 1
< 0.1%
308.9 1
< 0.1%
307.3 1
< 0.1%
302.6 1
< 0.1%
300 1
< 0.1%
299.4 1
< 0.1%
298.8 1
< 0.1%
298.2 1
< 0.1%

symptom_duration
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.8767
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:22:50.057579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15
Q322
95-th percentile28
Maximum29
Range28
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.4420628
Coefficient of variation (CV)0.56746878
Kurtosis-1.2120203
Mean14.8767
Median Absolute Deviation (MAD)7
Skewness0.015446318
Sum148767
Variance71.268424
MonotonicityNot monotonic
2025-04-14T22:22:50.135552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 404
 
4.0%
23 372
 
3.7%
27 370
 
3.7%
13 368
 
3.7%
7 368
 
3.7%
29 360
 
3.6%
2 357
 
3.6%
11 354
 
3.5%
16 354
 
3.5%
12 351
 
3.5%
Other values (19) 6342
63.4%
ValueCountFrequency (%)
1 404
4.0%
2 357
3.6%
3 346
3.5%
4 337
3.4%
5 340
3.4%
6 341
3.4%
7 368
3.7%
8 343
3.4%
9 340
3.4%
10 323
3.2%
ValueCountFrequency (%)
29 360
3.6%
28 322
3.2%
27 370
3.7%
26 334
3.3%
25 337
3.4%
24 340
3.4%
23 372
3.7%
22 335
3.4%
21 324
3.2%
20 321
3.2%

clinical_notes
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size717.3 KiB
fever and fatigue
1289 
blurred vision
1279 
joint pain
1269 
chest pain
1266 
dizziness and confusion
1236 
Other values (3)
3661 

Length

Max length23
Median length19
Mean length16.4387
Min length10

Characters and Unicode

Total characters164387
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowheadache and nausea
2nd rowabdominal discomfort
3rd rowfever and fatigue
4th rowjoint pain
5th rowchest pain

Common Values

ValueCountFrequency (%)
fever and fatigue 1289
12.9%
blurred vision 1279
12.8%
joint pain 1269
12.7%
chest pain 1266
12.7%
dizziness and confusion 1236
12.4%
abdominal discomfort 1231
12.3%
shortness of breath 1223
12.2%
headache and nausea 1207
12.1%

Length

2025-04-14T22:22:50.221318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:22:50.301900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
and 3732
15.0%
pain 2535
 
10.2%
fever 1289
 
5.2%
fatigue 1289
 
5.2%
blurred 1279
 
5.1%
vision 1279
 
5.1%
joint 1269
 
5.1%
chest 1266
 
5.1%
confusion 1236
 
5.0%
dizziness 1236
 
5.0%
Other values (7) 8545
34.2%

Most occurring characters

ValueCountFrequency (%)
n 16184
 
9.8%
a 16069
 
9.8%
14955
 
9.1%
i 13821
 
8.4%
e 13715
 
8.3%
s 12360
 
7.5%
o 11159
 
6.8%
d 9916
 
6.0%
r 7524
 
4.6%
t 7501
 
4.6%
Other values (12) 41183
25.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 164387
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 16184
 
9.8%
a 16069
 
9.8%
14955
 
9.1%
i 13821
 
8.4%
e 13715
 
8.3%
s 12360
 
7.5%
o 11159
 
6.8%
d 9916
 
6.0%
r 7524
 
4.6%
t 7501
 
4.6%
Other values (12) 41183
25.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 164387
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 16184
 
9.8%
a 16069
 
9.8%
14955
 
9.1%
i 13821
 
8.4%
e 13715
 
8.3%
s 12360
 
7.5%
o 11159
 
6.8%
d 9916
 
6.0%
r 7524
 
4.6%
t 7501
 
4.6%
Other values (12) 41183
25.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 164387
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 16184
 
9.8%
a 16069
 
9.8%
14955
 
9.1%
i 13821
 
8.4%
e 13715
 
8.3%
s 12360
 
7.5%
o 11159
 
6.8%
d 9916
 
6.0%
r 7524
 
4.6%
t 7501
 
4.6%
Other values (12) 41183
25.1%

department
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size664.5 KiB
General Medicine
2038 
Orthopedics
2033 
Cardiology
2000 
Neurology
1993 
Emergency
1936 

Length

Max length16
Median length11
Mean length11.0332
Min length9

Characters and Unicode

Total characters110332
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOrthopedics
2nd rowOrthopedics
3rd rowGeneral Medicine
4th rowCardiology
5th rowOrthopedics

Common Values

ValueCountFrequency (%)
General Medicine 2038
20.4%
Orthopedics 2033
20.3%
Cardiology 2000
20.0%
Neurology 1993
19.9%
Emergency 1936
19.4%

Length

2025-04-14T22:22:50.404097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:22:50.477331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
general 2038
16.9%
medicine 2038
16.9%
orthopedics 2033
16.9%
cardiology 2000
16.6%
neurology 1993
16.6%
emergency 1936
16.1%

Most occurring characters

ValueCountFrequency (%)
e 16050
14.5%
o 10019
 
9.1%
r 10000
 
9.1%
i 8109
 
7.3%
d 6071
 
5.5%
l 6031
 
5.5%
n 6012
 
5.4%
c 6007
 
5.4%
y 5929
 
5.4%
g 5929
 
5.4%
Other values (14) 30175
27.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 110332
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 16050
14.5%
o 10019
 
9.1%
r 10000
 
9.1%
i 8109
 
7.3%
d 6071
 
5.5%
l 6031
 
5.5%
n 6012
 
5.4%
c 6007
 
5.4%
y 5929
 
5.4%
g 5929
 
5.4%
Other values (14) 30175
27.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 110332
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 16050
14.5%
o 10019
 
9.1%
r 10000
 
9.1%
i 8109
 
7.3%
d 6071
 
5.5%
l 6031
 
5.5%
n 6012
 
5.4%
c 6007
 
5.4%
y 5929
 
5.4%
g 5929
 
5.4%
Other values (14) 30175
27.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 110332
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 16050
14.5%
o 10019
 
9.1%
r 10000
 
9.1%
i 8109
 
7.3%
d 6071
 
5.5%
l 6031
 
5.5%
n 6012
 
5.4%
c 6007
 
5.4%
y 5929
 
5.4%
g 5929
 
5.4%
Other values (14) 30175
27.3%

admission_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size650.6 KiB
Outpatient
6039 
Inpatient
2513 
Emergency
1448 

Length

Max length10
Median length10
Mean length9.6039
Min length9

Characters and Unicode

Total characters96039
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInpatient
2nd rowEmergency
3rd rowOutpatient
4th rowOutpatient
5th rowInpatient

Common Values

ValueCountFrequency (%)
Outpatient 6039
60.4%
Inpatient 2513
25.1%
Emergency 1448
 
14.5%

Length

2025-04-14T22:22:50.560124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:22:50.625436image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
outpatient 6039
60.4%
inpatient 2513
25.1%
emergency 1448
 
14.5%

Most occurring characters

ValueCountFrequency (%)
t 23143
24.1%
n 12513
13.0%
e 11448
11.9%
p 8552
 
8.9%
a 8552
 
8.9%
i 8552
 
8.9%
O 6039
 
6.3%
u 6039
 
6.3%
I 2513
 
2.6%
E 1448
 
1.5%
Other values (5) 7240
 
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 96039
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 23143
24.1%
n 12513
13.0%
e 11448
11.9%
p 8552
 
8.9%
a 8552
 
8.9%
i 8552
 
8.9%
O 6039
 
6.3%
u 6039
 
6.3%
I 2513
 
2.6%
E 1448
 
1.5%
Other values (5) 7240
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 96039
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 23143
24.1%
n 12513
13.0%
e 11448
11.9%
p 8552
 
8.9%
a 8552
 
8.9%
i 8552
 
8.9%
O 6039
 
6.3%
u 6039
 
6.3%
I 2513
 
2.6%
E 1448
 
1.5%
Other values (5) 7240
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 96039
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 23143
24.1%
n 12513
13.0%
e 11448
11.9%
p 8552
 
8.9%
a 8552
 
8.9%
i 8552
 
8.9%
O 6039
 
6.3%
u 6039
 
6.3%
I 2513
 
2.6%
E 1448
 
1.5%
Other values (5) 7240
 
7.5%

insurance_status
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size628.0 KiB
Insured
8509 
Uninsured
1491 

Length

Max length9
Median length7
Mean length7.2982
Min length7

Characters and Unicode

Total characters72982
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInsured
2nd rowInsured
3rd rowInsured
4th rowInsured
5th rowUninsured

Common Values

ValueCountFrequency (%)
Insured 8509
85.1%
Uninsured 1491
 
14.9%

Length

2025-04-14T22:22:50.702866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:22:50.768160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
insured 8509
85.1%
uninsured 1491
 
14.9%

Most occurring characters

ValueCountFrequency (%)
n 11491
15.7%
s 10000
13.7%
u 10000
13.7%
r 10000
13.7%
e 10000
13.7%
d 10000
13.7%
I 8509
11.7%
U 1491
 
2.0%
i 1491
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72982
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 11491
15.7%
s 10000
13.7%
u 10000
13.7%
r 10000
13.7%
e 10000
13.7%
d 10000
13.7%
I 8509
11.7%
U 1491
 
2.0%
i 1491
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72982
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 11491
15.7%
s 10000
13.7%
u 10000
13.7%
r 10000
13.7%
e 10000
13.7%
d 10000
13.7%
I 8509
11.7%
U 1491
 
2.0%
i 1491
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72982
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 11491
15.7%
s 10000
13.7%
u 10000
13.7%
r 10000
13.7%
e 10000
13.7%
d 10000
13.7%
I 8509
11.7%
U 1491
 
2.0%
i 1491
 
2.0%

diagnosis_code
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size595.8 KiB
D163
2510 
D141
1939 
D196
1537 
D185
1079 
D198
995 
Other values (3)
1940 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters40000
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD141
2nd rowD196
3rd rowD165
4th rowD196
5th rowD196

Common Values

ValueCountFrequency (%)
D163 2510
25.1%
D141 1939
19.4%
D196 1537
15.4%
D185 1079
10.8%
D198 995
 
10.0%
D119 952
 
9.5%
D165 499
 
5.0%
D143 489
 
4.9%

Length

2025-04-14T22:22:50.833167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:22:50.905182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
d163 2510
25.1%
d141 1939
19.4%
d196 1537
15.4%
d185 1079
10.8%
d198 995
 
10.0%
d119 952
 
9.5%
d165 499
 
5.0%
d143 489
 
4.9%

Most occurring characters

ValueCountFrequency (%)
1 12891
32.2%
D 10000
25.0%
6 4546
 
11.4%
9 3484
 
8.7%
3 2999
 
7.5%
4 2428
 
6.1%
8 2074
 
5.2%
5 1578
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 12891
32.2%
D 10000
25.0%
6 4546
 
11.4%
9 3484
 
8.7%
3 2999
 
7.5%
4 2428
 
6.1%
8 2074
 
5.2%
5 1578
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 12891
32.2%
D 10000
25.0%
6 4546
 
11.4%
9 3484
 
8.7%
3 2999
 
7.5%
4 2428
 
6.1%
8 2074
 
5.2%
5 1578
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 12891
32.2%
D 10000
25.0%
6 4546
 
11.4%
9 3484
 
8.7%
3 2999
 
7.5%
4 2428
 
6.1%
8 2074
 
5.2%
5 1578
 
3.9%

bp_hr_interaction
Real number (ℝ)

High correlation 

Distinct9609
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9005.202
Minimum3408.75
Maximum16848
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:22:50.997714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3408.75
5-th percentile6404.9685
Q17850.625
median8920.7
Q310077.062
95-th percentile11904.15
Maximum16848
Range13439.25
Interquartile range (IQR)2226.4375

Descriptive statistics

Standard deviation1673.1703
Coefficient of variation (CV)0.18580042
Kurtosis0.044800036
Mean9005.202
Median Absolute Deviation (MAD)1114.3
Skewness0.26785306
Sum90052020
Variance2799498.8
MonotonicityNot monotonic
2025-04-14T22:22:51.095349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8289.54 3
 
< 0.1%
8755.2 3
 
< 0.1%
8898.24 3
 
< 0.1%
8446.02 3
 
< 0.1%
8787.84 3
 
< 0.1%
8722.74 3
 
< 0.1%
8905.14 3
 
< 0.1%
8562.9 3
 
< 0.1%
8396.28 3
 
< 0.1%
9853.2 3
 
< 0.1%
Other values (9599) 9970
99.7%
ValueCountFrequency (%)
3408.75 1
< 0.1%
3452.68 1
< 0.1%
3628.8 1
< 0.1%
3996.27 1
< 0.1%
4107.2 1
< 0.1%
4371.68 1
< 0.1%
4388.8 1
< 0.1%
4390.2 1
< 0.1%
4411.88 1
< 0.1%
4413.2 1
< 0.1%
ValueCountFrequency (%)
16848 1
< 0.1%
16212.14 1
< 0.1%
15665.85 1
< 0.1%
15651 1
< 0.1%
15616.71 1
< 0.1%
15585.4 1
< 0.1%
15470.72 1
< 0.1%
14983.65 1
< 0.1%
14733.16 1
< 0.1%
14717.24 1
< 0.1%

bp_glucose_ratio
Real number (ℝ)

High correlation 

Distinct9852
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2846013
Minimum0.47418398
Maximum13.166667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:22:51.185741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.47418398
5-th percentile0.79296594
Q10.99437991
median1.1881492
Q31.4541427
95-th percentile2.0572817
Maximum13.166667
Range12.692483
Interquartile range (IQR)0.4597628

Descriptive statistics

Standard deviation0.48651987
Coefficient of variation (CV)0.37873221
Kurtosis67.678418
Mean1.2846013
Median Absolute Deviation (MAD)0.22139687
Skewness4.9305291
Sum12846.013
Variance0.23670159
MonotonicityNot monotonic
2025-04-14T22:22:51.282075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 10
 
0.1%
1.25 5
 
0.1%
1.230769231 3
 
< 0.1%
1.264893617 3
 
< 0.1%
1.6 3
 
< 0.1%
1.666666667 3
 
< 0.1%
1.111111111 3
 
< 0.1%
0.9959718026 2
 
< 0.1%
1.042087542 2
 
< 0.1%
1.523809524 2
 
< 0.1%
Other values (9842) 9964
99.6%
ValueCountFrequency (%)
0.4741839763 1
< 0.1%
0.4814241486 1
< 0.1%
0.4857142857 1
< 0.1%
0.4944912508 1
< 0.1%
0.5104844541 1
< 0.1%
0.5339339339 1
< 0.1%
0.5390869293 1
< 0.1%
0.5414398064 1
< 0.1%
0.5513833992 1
< 0.1%
0.55532926 1
< 0.1%
ValueCountFrequency (%)
13.16666667 1
< 0.1%
9.483443709 1
< 0.1%
8.477124183 1
< 0.1%
8.398648649 1
< 0.1%
8.296296296 1
< 0.1%
6.671428571 1
< 0.1%
6.091370558 1
< 0.1%
5.900763359 1
< 0.1%
5.873170732 1
< 0.1%
5.768115942 1
< 0.1%

duration_per_hr
Real number (ℝ)

High correlation 

Distinct6065
Distinct (%)60.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19942696
Minimum0.0091324201
Maximum0.68292683
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:22:51.373587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.0091324201
5-th percentile0.024600246
Q10.099418584
median0.19449044
Q30.29285299
95-th percentile0.39187228
Maximum0.68292683
Range0.67379441
Interquartile range (IQR)0.19343441

Descriptive statistics

Standard deviation0.11734438
Coefficient of variation (CV)0.58840782
Kurtosis-0.85571272
Mean0.19942696
Median Absolute Deviation (MAD)0.096708006
Skewness0.20958758
Sum1994.2696
Variance0.013769705
MonotonicityNot monotonic
2025-04-14T22:22:51.467034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3125 15
 
0.1%
0.3333333333 14
 
0.1%
0.1666666667 11
 
0.1%
0.2857142857 11
 
0.1%
0.1111111111 10
 
0.1%
0.1818181818 10
 
0.1%
0.1538461538 10
 
0.1%
0.2631578947 10
 
0.1%
0.1515151515 10
 
0.1%
0.4166666667 9
 
0.1%
Other values (6055) 9890
98.9%
ValueCountFrequency (%)
0.009132420091 1
< 0.1%
0.009871668312 1
< 0.1%
0.009881422925 1
< 0.1%
0.009950248756 1
< 0.1%
0.01012145749 1
< 0.1%
0.01014198783 1
< 0.1%
0.01020408163 2
< 0.1%
0.01028806584 1
< 0.1%
0.01037344398 1
< 0.1%
0.01043841336 1
< 0.1%
ValueCountFrequency (%)
0.6829268293 1
< 0.1%
0.5673758865 1
< 0.1%
0.5652173913 1
< 0.1%
0.5633802817 1
< 0.1%
0.56 1
< 0.1%
0.5523809524 1
< 0.1%
0.545112782 1
< 0.1%
0.5446623094 1
< 0.1%
0.5380333952 1
< 0.1%
0.5303030303 1
< 0.1%

pca1
Real number (ℝ)

High correlation  Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.4210855 × 10-18
Minimum-3.8564275
Maximum3.5560304
Zeros0
Zeros (%)0.0%
Negative4991
Negative (%)49.9%
Memory size78.2 KiB
2025-04-14T22:22:51.559580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-3.8564275
5-th percentile-1.6712525
Q1-0.68633492
median0.0016482659
Q30.68907255
95-th percentile1.6817608
Maximum3.5560304
Range7.4124579
Interquartile range (IQR)1.3754075

Descriptive statistics

Standard deviation1.0155934
Coefficient of variation (CV)-7.146603 × 1017
Kurtosis-0.054121309
Mean-1.4210855 × 10-18
Median Absolute Deviation (MAD)0.68773505
Skewness-0.010293349
Sum1.7941204 × 10-13
Variance1.0314299
MonotonicityNot monotonic
2025-04-14T22:22:51.654280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6080990221 1
 
< 0.1%
2.512214335 1
 
< 0.1%
-0.4051359761 1
 
< 0.1%
-2.968708563 1
 
< 0.1%
0.3872240945 1
 
< 0.1%
0.6454981956 1
 
< 0.1%
-1.011126221 1
 
< 0.1%
0.1357537134 1
 
< 0.1%
-0.2662557181 1
 
< 0.1%
-1.082001411 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
-3.856427539 1
< 0.1%
-3.833893972 1
< 0.1%
-3.785308854 1
< 0.1%
-3.77699147 1
< 0.1%
-3.354287856 1
< 0.1%
-3.344189025 1
< 0.1%
-3.243180826 1
< 0.1%
-3.219826576 1
< 0.1%
-3.131529852 1
< 0.1%
-3.115082028 1
< 0.1%
ValueCountFrequency (%)
3.556030362 1
< 0.1%
3.538797233 1
< 0.1%
3.18578794 1
< 0.1%
3.175855747 1
< 0.1%
3.168475046 1
< 0.1%
3.11398811 1
< 0.1%
3.100294964 1
< 0.1%
3.036329916 1
< 0.1%
3.033785069 1
< 0.1%
3.024745713 1
< 0.1%

pca2
Real number (ℝ)

High correlation  Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4158453 × 10-17
Minimum-3.6818499
Maximum3.4352066
Zeros0
Zeros (%)0.0%
Negative5006
Negative (%)50.1%
Memory size78.2 KiB
2025-04-14T22:22:51.746694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-3.6818499
5-th percentile-1.6696831
Q1-0.69276688
median-0.0011759472
Q30.69401125
95-th percentile1.6714215
Maximum3.4352066
Range7.1170565
Interquartile range (IQR)1.3867781

Descriptive statistics

Standard deviation1.0133297
Coefficient of variation (CV)4.1945141 × 1016
Kurtosis-0.1007268
Mean2.4158453 × 10-17
Median Absolute Deviation (MAD)0.69416779
Skewness-0.026855712
Sum2.7000624 × 10-13
Variance1.0268371
MonotonicityNot monotonic
2025-04-14T22:22:51.840126image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.8633528449 1
 
< 0.1%
0.3900941681 1
 
< 0.1%
0.2679862773 1
 
< 0.1%
0.5268908697 1
 
< 0.1%
-2.006806542 1
 
< 0.1%
1.033108587 1
 
< 0.1%
-0.8806317605 1
 
< 0.1%
0.9196153406 1
 
< 0.1%
-0.5958343407 1
 
< 0.1%
-1.640295455 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
-3.681849864 1
< 0.1%
-3.535715733 1
< 0.1%
-3.454774898 1
< 0.1%
-3.379740781 1
< 0.1%
-3.300899246 1
< 0.1%
-3.294560814 1
< 0.1%
-3.253245638 1
< 0.1%
-3.227835549 1
< 0.1%
-3.19527389 1
< 0.1%
-3.176947553 1
< 0.1%
ValueCountFrequency (%)
3.435206644 1
< 0.1%
3.411808754 1
< 0.1%
3.377153945 1
< 0.1%
3.292105602 1
< 0.1%
3.275448621 1
< 0.1%
3.084011115 1
< 0.1%
3.043875728 1
< 0.1%
3.034376742 1
< 0.1%
2.991964088 1
< 0.1%
2.980582689 1
< 0.1%

cluster
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.5 KiB
0
2520 
3
2513 
1
2499 
2
2468 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row0
4th row3
5th row1

Common Values

ValueCountFrequency (%)
0 2520
25.2%
3 2513
25.1%
1 2499
25.0%
2 2468
24.7%

Length

2025-04-14T22:22:51.924576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:22:51.990442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2520
25.2%
3 2513
25.1%
1 2499
25.0%
2 2468
24.7%

Most occurring characters

ValueCountFrequency (%)
0 2520
25.2%
3 2513
25.1%
1 2499
25.0%
2 2468
24.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2520
25.2%
3 2513
25.1%
1 2499
25.0%
2 2468
24.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2520
25.2%
3 2513
25.1%
1 2499
25.0%
2 2468
24.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2520
25.2%
3 2513
25.1%
1 2499
25.0%
2 2468
24.7%

Interactions

2025-04-14T22:22:46.990720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:39.314679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:40.151210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:40.885951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:41.638886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:42.369454image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:43.096460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:43.909026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:44.648947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:45.379736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:46.230704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:47.051734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:39.396461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:40.212298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:40.942082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:41.700767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:42.431389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:43.238971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:43.971165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:44.711639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:45.441545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:46.293171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:47.119509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:39.471191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:40.279562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:41.005792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:41.768029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:42.499092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:43.307098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:44.038583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:44.778432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:45.510949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:46.360105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:47.177825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:39.553853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:40.338190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:41.059929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:41.827728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:42.558279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:43.367097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:44.098934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:44.837835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:45.673367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:46.419333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:47.246311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:39.631411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:40.405364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:41.122506image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:41.893162image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:42.624807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:43.433737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:44.167301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:44.903515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:45.744541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:46.486732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:47.312867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:39.695422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:40.472715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:41.184756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:41.960720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:42.690804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:43.501987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:44.235343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:44.971098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:45.814246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:46.553273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:47.380828image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:39.824343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:40.541080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:41.247297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:42.027371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:42.757653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:43.567211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:44.305489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:45.038192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:45.883107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:46.619221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:47.448354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:39.890111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:40.610397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:41.381236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:42.096423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:42.824978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:43.634876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:44.371988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:45.106920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:45.951377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:46.687871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:47.515816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:39.954215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:40.677066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:41.444688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:42.162497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:42.891551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:43.701683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:44.439428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:45.172182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:46.021197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:46.754893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:47.585570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:40.021244image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:40.748167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:41.509730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:42.232940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:42.960787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:43.771387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:44.510243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:45.243092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:46.089555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:46.824444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:47.653282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:40.085579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:40.815105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:41.574974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:42.300596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:43.028624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:43.840434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:44.580377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:45.310169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:46.160325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:22:46.922088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-04-14T22:22:52.055280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
admission_typeageblood_pressurebp_glucose_ratiobp_hr_interactioncholesterol_levelclinical_notesclusterdepartmentdiagnosis_codeduration_per_hrgenderglucose_levelheart_rateinsurance_statuspca1pca2symptom_duration
admission_type1.0000.0140.0100.0110.0000.0000.0130.0000.0150.0210.0000.0000.0000.0000.0000.0000.0000.000
age0.0141.0000.0040.0130.0060.0160.0000.4610.0160.0180.0020.000-0.0120.0070.006-0.2590.3840.003
blood_pressure0.0100.0041.0000.4300.675-0.0040.0070.1270.0190.000-0.0140.0000.0050.0250.0000.5480.348-0.010
bp_glucose_ratio0.0110.0130.4301.0000.289-0.0070.0130.0430.0000.0000.0000.000-0.8810.0050.000-0.049-0.016-0.000
bp_hr_interaction0.0000.0060.6750.2891.0000.0030.0000.1970.0080.000-0.1510.0240.0120.7220.0000.6580.597-0.004
cholesterol_level0.0000.016-0.004-0.0070.0031.0000.0040.2410.0000.0000.0190.0070.0080.0030.019-0.3780.5220.020
clinical_notes0.0130.0000.0070.0130.0000.0041.0000.0080.0110.0080.0000.0000.0040.0140.0180.0000.0000.018
cluster0.0000.4610.1270.0430.1970.2410.0081.0000.0000.0130.4370.0000.1240.1580.0000.2500.4340.456
department0.0150.0160.0190.0000.0080.0000.0110.0001.0000.0100.0000.0000.0000.0100.0000.0000.0120.005
diagnosis_code0.0210.0180.0000.0000.0000.0000.0080.0130.0101.0000.0010.0050.0040.0030.0000.0070.0060.000
duration_per_hr0.0000.002-0.0140.000-0.1510.0190.0000.4370.0000.0011.0000.000-0.006-0.1980.000-0.5120.2650.972
gender0.0000.0000.0000.0000.0240.0070.0000.0000.0000.0050.0001.0000.0000.0050.0000.0110.0000.000
glucose_level0.000-0.0120.005-0.8810.0120.0080.0040.1240.0000.004-0.0060.0001.0000.0070.0130.3290.194-0.004
heart_rate0.0000.0070.0250.0050.7220.0030.0140.1580.0100.003-0.1980.0050.0071.0000.0000.3950.4940.003
insurance_status0.0000.0060.0000.0000.0000.0190.0180.0000.0000.0000.0000.0000.0130.0001.0000.0000.0000.000
pca10.000-0.2590.548-0.0490.658-0.3780.0000.2500.0000.007-0.5120.0110.3290.3950.0001.000-0.007-0.441
pca20.0000.3840.348-0.0160.5970.5220.0000.4340.0120.0060.2650.0000.1940.4940.000-0.0071.0000.378
symptom_duration0.0000.003-0.010-0.000-0.0040.0200.0180.4560.0050.0000.9720.000-0.0040.0030.000-0.4410.3781.000

Missing values

2025-04-14T22:22:47.764134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-14T22:22:47.956700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

patient_idagegenderblood_pressureheart_rateglucose_levelcholesterol_levelsymptom_durationclinical_notesdepartmentadmission_typeinsurance_statusdiagnosis_codebp_hr_interactionbp_glucose_ratioduration_per_hrpca1pca2cluster
0PAT0000080Female125.961.3136.395.715headache and nauseaOrthopedicsInpatientInsuredD1417717.670.9169700.2407700.608099-0.8633530
1PAT000017Male136.968.8126.9133.57abdominal discomfortOrthopedicsEmergencyInsuredD1969418.721.0703670.1002871.972034-1.2281512
2PAT0000234Male103.768.0117.8153.323fever and fatigueGeneral MedicineOutpatientInsuredD1657051.600.8728960.333333-0.702154-0.7446490
3PAT0000334Female135.665.0102.4203.45joint painCardiologyOutpatientInsuredD1968814.001.3114120.0757580.586714-0.4040993
4PAT0000432Female151.678.8141.5251.012chest painOrthopedicsInpatientUninsuredD19611946.081.0638600.1503761.4637091.9115261
5PAT000054Male131.580.1131.5156.417dizziness and confusionCardiologyOutpatientInsuredD19610533.150.9924530.2096181.586058-0.0329441
6PAT0000640Female131.970.4138.7219.116headache and nauseaNeurologyOutpatientInsuredD1419285.760.9441660.2240900.3792020.8668421
7PAT0000727Female122.594.4117.4269.517dizziness and confusionNeurologyEmergencyInsuredD14111564.001.0346280.1781970.3080082.2178851
8PAT000086Female112.157.5103.1242.627blurred visionNeurologyInpatientInsuredD1856445.751.0768490.461538-1.812907-0.2310491
9PAT0000972Male131.475.690.0163.87fever and fatigueEmergencyOutpatientInsuredD1419933.841.4439560.0913840.5966760.0547483
patient_idagegenderblood_pressureheart_rateglucose_levelcholesterol_levelsymptom_durationclinical_notesdepartmentadmission_typeinsurance_statusdiagnosis_codebp_hr_interactionbp_glucose_ratioduration_per_hrpca1pca2cluster
9990PAT0999084Female105.885.3122.6248.912abdominal discomfortCardiologyEmergencyInsuredD1659024.740.8559870.139050-0.7269021.7394193
9991PAT0999131Male107.977.4105.2199.67headache and nauseaCardiologyOutpatientInsuredD1198351.461.0160080.0892860.067431-0.4166182
9992PAT0999251Male93.379.7101.1142.310chest painGeneral MedicineOutpatientUninsuredD1987436.010.9138100.123916-0.231204-1.0164252
9993PAT0999382Male123.273.1105.2139.14abdominal discomfortNeurologyInpatientInsuredD1639005.921.1600750.0539810.690955-0.4644613
9994PAT0999427Female109.085.5150.3261.027dizziness and confusionCardiologyOutpatientUninsuredD1199319.500.7204230.312139-0.5424402.0464881
9995PAT0999584Male144.959.0101.4164.81joint painOrthopedicsOutpatientInsuredD1638549.101.4150390.0166670.752763-0.4555783
9996PAT0999663Other126.166.0107.3139.414shortness of breathCardiologyOutpatientInsuredD1638322.601.1643580.2089550.211566-0.5626000
9997PAT0999778Female117.487.9151.5227.64abdominal discomfortOrthopedicsOutpatientInsuredD11910319.460.7698360.0449940.8787001.6424663
9998PAT0999877Female110.470.698.4174.127abdominal discomfortGeneral MedicineOutpatientInsuredD1197794.241.1106640.377095-1.4518460.4845020
9999PAT0999918Male97.653.5143.298.928dizziness and confusionNeurologyInpatientUninsuredD1965221.600.6768380.513761-0.740673-2.1597802